df_BA %>%
ggplot(aes(date, confirmed, col = version))+
geom_line()+
labs(title = "Differences in Old (60% missing in CABA) vs New (Data direct from City) over time")
df_BA %>%
filter(date == max(date)) %>%
select(version, date, confirmed) %>%
pivot_wider(names_from = "version",
values_from = "confirmed") %>%
mutate(rel_change =round( ((new-old)/old )*100 ,2) )
Cumulative relative change: New Data has 17.5% more cases for Beunos Aires
df_l2 = df_BA_L2 %>%
filter(date == max(date)) %>%
select(version,salid2,loc, date, confirmed) %>%
pivot_wider(names_from = "version",
values_from = "confirmed") %>%
arrange()%>%
mutate(rel_change = round( ((new-old)/old )*100 ,2) ) %>%
arrange(desc(rel_change))
df_l2
p = df_l2%>%
ggplot(aes(old, new, group = salid2, text =loc))+
geom_point()
library(plotly)
ggplotly(p)